The Ethics of Dying: Deciphering Pandemic-Resultant Pressures That Influence Elderly Patients’ Medical Assistance in Dying (MAiD) Decisions
Bibliographic record
Abstract
The objective of medicine is to provide humans with the best possible health outcomes from the beginning to the end of life. If the continuation of life becomes unbearable, some may evaluate procedures to end their lives prematurely. One such procedure is Medical Assistance in Dying (MAiD), and it is hotly contended in many spheres of society. From legal to personal perspectives, there are strong arguments for its implementation and prohibition. This article intends to add to this rich discourse by exploring MAiD in the context of our current pandemic-ridden society as new pressures from social isolation and guilt threaten the autonomy of vulnerable elderly patients. Although autonomy is of chief importance, variables within our current context undermine otherwise independent decisions. Many older individuals are isolated from their social network, resulting in a decline in their mental health. Individuals in such a state are more likely to request a MAiD outcome. Furthermore, overwhelmed healthcare systems may not adequately address this state, which would normally have prompted a mental health intervention. The future of MAiD is far from settled and careful consideration must be given as new contexts come to light, such as those outlined in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".